[1]}, [2]} propose extensions of
continuous normalizing flows to the Riemannian manifold setting. These
are flexible with respect to the choice of manifold, but their
representational capacity is not well-understood and solving ODEs on
manifolds can be expensive.
In parallel, [3]} proposed a method for
simultaneously learning the manifold data lives on and a normalizing
flow on the learned
manifold.
[4]} consider hyperbolic normalizing flows.
